Estimated Phytoplankton Size

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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys

import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive, plot_oni_index_th
from ind_setup.plotting import plot_base_map, plot_map_subplots, add_oni_cat, plot_bar_probs, fontsize

sys.path.append("../../../functions")
from data_downloaders import download_ERDDAP_data, download_oni_index

Setup

Define area of interest

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

EEZ shapefile

shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

Download Data

DATASET: https://oceanwatch.pifsc.noaa.gov/erddap/info/md50_exp/index.html

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"

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base_url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/md50_exp.csv'
dataset_id = 'MD50'

if update_data:
    date_ini = '1998-01-01T00:00:00Z'
    date_end = '2023-12-01T00:00:00Z'
    data = download_ERDDAP_data(base_url, dataset_id, date_ini, date_end, lon_range, lat_range)
    data_xr = data.set_index(['latitude', 'longitude', 'time']).to_xarray()
    data_xr['time'] = pd.to_datetime(data_xr.time)
    data_xr = data_xr.coarsen(longitude=2, latitude=2, boundary = 'pad').mean()
    data_xr.to_netcdf(op.join(path_data, f'griddap_{dataset_id}.nc'))
else:
    data_xr = xr.open_dataset(op.join(path_data, f'griddap_{dataset_id}.nc'))

Analysis

Plotting

Average

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'YlGnBu', vmin = 0.8, vmax = 1.3)
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label='Phytoplankton size MD50 (µm ESD)')
plt.savefig(op.join(path_figs, 'F16_phytoplankton_mean_map.png'), dpi=300, bbox_inches='tight')

Annual average

data_y = data_xr.resample(time='1YE').mean()
fig = plot_map_subplots(data_y, 'MD50', shp_eez = shp_eez, cmap = 'YlGnBu', vmin = 0.4, vmax = 1.6, cbar = 1)

Annual anomaly

data_an = data_y - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.3, vmax=.3, cbar = 1)

Average over area

dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : 'Median Phytoplankton Size - MEAN AREA'},]

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12), 
                                 label_yaxes = 'Phytoplankton MD50 (µm ESD)');
fig.write_html(op.join(path_figs, 'F16_phytoplankton_mean_trend.html'), include_plotlyjs="cdn")

Top 10 years

data_mean = data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe()
data_mean = data_mean.resample('YE').mean()
top_10 = data_mean.sort_values(by='MD50', ascending=False).head(10)

fig, ax, trend = plot_bar_probs(x=data_mean.index.year, y=data_mean.MD50, trendline=True,
                                y_label='Phytoplankton MD50 (µm ESD)', figsize=[15, 4], return_trend=True)
ax.set_ylim(data_mean.MD50.min(), data_mean.MD50.max()+.01)

im = ax.scatter(top_10.index.year, top_10.MD50,
                c=top_10.MD50.values, s=100, ec='pink', cmap='rainbow', label='Top 10 warmest years')
plt.colorbar(im, pad=0, shrink=.7).set_label('Absolute MD50 (µm ESD)', fontsize=fontsize)
ax.set_title('Annual Mean MD50', fontsize=15);

Timeseries at a given point

loc = [7.35, 134.48]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'Median Phytoplankton Size at [{loc[0]}, {loc[1]}]'},]

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()

<matplotlib.legend.Legend at 0x180c88410>

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12),
                                 label_yaxes = 'Phytoplankton MD50 (µm ESD)');

ONI index analysis

if update_data:
    p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
    df1 = download_oni_index(p_data)
    df1.to_pickle(op.join(path_data, 'oni_index.pkl'))
else:
    df1 = pd.read_pickle(op.join(path_data, 'oni_index.pkl'))

lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)

Group by ONI category

df1 = add_oni_cat(df1, lims = lims)
df1['ONI'] = df1['oni_cat']
data_xr['ONI'] = (('time'), df1.iloc[np.intersect1d(data_xr.time, df1.index, return_indices=True)[2]].ONI.values)
data_xr['ONI_cat'] = (('time'), np.where(data_xr.ONI < lims[0], -1, np.where(data_xr.ONI > lims[1], 1, 0)))
data_oni = data_xr.groupby('ONI_cat').mean()

Average

fig = plot_map_subplots(data_oni, dataset_id, shp_eez = shp_eez, cmap = 'YlGnBu', vmin = 0.8, vmax = 1.3, 
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)
plt.savefig(op.join(path_figs, 'F16_phytoplankton_ENSO.png'), dpi=300, bbox_inches='tight')

Anomaly

data_an = data_oni - data_xr.mean(dim='time')
fig = plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.1, vmax=.1,
                  sub_plot= [1, 3], figsize = (20, 9),  cbar = True, cbar_pad = 0.1,
                  titles = ['La Niña', 'Neutral', 'El Niño'],)